On Testability of Missing Data Mechanisms in Incomplete Data Sets

This article is concerned with the question of whether the missing data mechanism routinely referred to as missing completely at random (MCAR) is statistically examinable via a test for lack of distributional differences between groups with observed and missing data, and related consequences. A discussion is initially provided, from a formal logic standpoint, of the distinction between necessary conditions and sufficient conditions. This distinction is used to argue then that testing for lack of these group distributional differences is not a test for MCAR, and an example is given. The view is next presented that the desirability of MCAR has been frequently overrated in empirical research. The article is finalized with a reference to principled, likelihood-based methods for analyzing incomplete data sets in social and behavioral research.